{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 导入包\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# 基础的数据处理工具\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 可视化\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 处理python时间函数\n",
    "import datetime\n",
    "\n",
    "# 处理nc数据\n",
    "import netCDF4 as nc\n",
    "from netCDF4 import num2date\n",
    "\n",
    "# 处理网格数据，shp之类的\n",
    "import geopandas as gpd\n",
    "\n",
    "# 处理tiff文件\n",
    "import rasterio\n",
    "\n",
    "# gis的一些逻辑判断\n",
    "from shapely.geometry import Point\n",
    "\n",
    "# 设置投影坐标系等\n",
    "from cartopy import crs as ccrs\n",
    "\n",
    "# 打印进度条\n",
    "from tqdm import tqdm\n",
    "\n",
    "tqdm.pandas()\n",
    "\n",
    "# 并行\n",
    "\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "# 检测系统\n",
    "\n",
    "import platform\n",
    "\n",
    "# matplotlib 显示中文的问题\n",
    "if platform.system() == 'Darwin':\n",
    "    plt.rcParams[\"font.family\"] = 'Arial Unicode MS'\n",
    "elif platform.system() == 'Windows':\n",
    "    plt.rcParams[\"font.family\"] = 'SimHei'\n",
    "else:\n",
    "    pass"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "nc_data = nc.Dataset(\"./数据集/GIS实践3/2016_2020.nc\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**********************************************************************\n",
      "<class 'netCDF4._netCDF4.Variable'>\n",
      "float32 longitude(longitude)\n",
      "    units: degrees_east\n",
      "    long_name: longitude\n",
      "unlimited dimensions: \n",
      "current shape = (31,)\n",
      "filling on, default _FillValue of 9.969209968386869e+36 used\n",
      "**********************************************************************\n",
      "<class 'netCDF4._netCDF4.Variable'>\n",
      "float32 latitude(latitude)\n",
      "    units: degrees_north\n",
      "    long_name: latitude\n",
      "unlimited dimensions: \n",
      "current shape = (21,)\n",
      "filling on, default _FillValue of 9.969209968386869e+36 used\n",
      "**********************************************************************\n",
      "<class 'netCDF4._netCDF4.Variable'>\n",
      "int32 time(time)\n",
      "    units: hours since 1900-01-01 00:00:00.0\n",
      "    long_name: time\n",
      "    calendar: gregorian\n",
      "unlimited dimensions: \n",
      "current shape = (43848,)\n",
      "filling on, default _FillValue of -2147483647 used\n",
      "**********************************************************************\n",
      "<class 'netCDF4._netCDF4.Variable'>\n",
      "int16 u10(time, latitude, longitude)\n",
      "    scale_factor: 0.00043007790966836365\n",
      "    add_offset: 1.9074540284279784\n",
      "    _FillValue: -32767\n",
      "    missing_value: -32767\n",
      "    units: m s**-1\n",
      "    long_name: 10 metre U wind component\n",
      "unlimited dimensions: \n",
      "current shape = (43848, 21, 31)\n",
      "filling on\n",
      "**********************************************************************\n",
      "<class 'netCDF4._netCDF4.Variable'>\n",
      "int16 v10(time, latitude, longitude)\n",
      "    scale_factor: 0.00042479520620217106\n",
      "    add_offset: 0.4807751512250239\n",
      "    _FillValue: -32767\n",
      "    missing_value: -32767\n",
      "    units: m s**-1\n",
      "    long_name: 10 metre V wind component\n",
      "unlimited dimensions: \n",
      "current shape = (43848, 21, 31)\n",
      "filling on\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-831ad1beb57c>:3: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n",
      "  print(item)\n"
     ]
    }
   ],
   "source": [
    "for item in nc_data.variables.values():\n",
    "    print('*' * 70)\n",
    "    print(item)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "dict_keys(['longitude', 'latitude', 'time', 'u10', 'v10'])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc_data.variables.keys()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-16fd3837c296>:1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  raw_latitude = np.array(nc_data.variables['latitude'])\n",
      "<ipython-input-7-16fd3837c296>:2: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  raw_longitude = np.array(nc_data.variables['longitude'])\n",
      "<ipython-input-7-16fd3837c296>:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  raw_time = np.array(nc_data.variables['time'])\n",
      "<ipython-input-7-16fd3837c296>:4: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  raw_u10 = np.array(nc_data.variables['u10'])\n",
      "<ipython-input-7-16fd3837c296>:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  raw_v10 = np.array(nc_data.variables['v10'])\n"
     ]
    }
   ],
   "source": [
    "raw_latitude = np.array(nc_data.variables['latitude'])\n",
    "raw_longitude = np.array(nc_data.variables['longitude'])\n",
    "raw_time = np.array(nc_data.variables['time'])\n",
    "raw_u10 = np.array(nc_data.variables['u10'])\n",
    "raw_v10 = np.array(nc_data.variables['v10'])\n",
    "\n",
    "missing_u10_value = nc_data.variables['u10'].missing_value\n",
    "missing_v10_value = nc_data.variables['v10'].missing_value"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "raw_v10[raw_v10 == missing_v10_value] = np.nan\n",
    "raw_u10[raw_u10 == missing_u10_value] = np.nan"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 绘制风场图\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "BIG_Longitude, BIG_LATITUDE = np.meshgrid(raw_longitude, raw_latitude)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.quiver.Quiver at 0x7f9f1e2863a0>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cartopy import crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "projection = ccrs.LambertConformal(central_longitude=np.mean(raw_longitude),\n",
    "                                   central_latitude=np.mean(raw_latitude))\n",
    "\n",
    "fig, ax = plt.subplots(subplot_kw={'projection': projection})\n",
    "\n",
    "ax.quiver(BIG_Longitude, BIG_LATITUDE, raw_u10[0, :, :], raw_v10[0, :, :])\n",
    "# ax.gridlines(draw_labels=True,linewidth=2, color='gray', alpha=0.5, linestyle='--')\n",
    "# ax.add_feature(cfeature.LAND.with_scale('50m'))\n",
    "# ax.add_feature(cfeature.COASTLINE.with_scale('50m'), lw=0.3)\n",
    "# ax.add_feature(cfeature.OCEAN.with_scale('50m'))\n",
    "# ax.streamplot(BIG_Longitude, BIG_LATITUDE, raw_u10[0, :, :], raw_v10[0, :, :])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}